Temporal artery biopsy for giant cell arteritis.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the influence of temporal artery biopsy (TAB) techniques on establishing a diagnosis of giant cell arteritis (GCA). METHODS: A retrospective review of 141 TAB pathology records from 1996 to 2002 was conducted. Histopathology slides on 136 TAB were reviewed by a single, independent, blinded pathologist. RESULTS: The population included 101 (71.6%) women, mean age 75.8 years (range 45-92), and 40 men, mean age 73.9 years (range 47-90). The mean length of a TAB sample after formalin fixation was 1.76 cm (range 0.1-5.3). Surgeons performing the TAB represented 6 disciplines. Ophthalmologists had the largest volume, at 78 biopsies (55.3%), and the longest segments of artery, with a mean length of 2.37 cm (range 0.4-5.3) (p < 0.001). Comparison of biopsy interpretation provided a kappa coefficient of 0.8 (95% CI 0.69, 0.91). The 38 (27%) positive biopsies had a mean length of 2.07 cm (SD 1.1), and the 98 negative biopsies a mean length of 1.69 cm (SD 1.04) (p = 0.058). Biopsies < 1.0 cm length (n = 35, 25.7%) were less likely to be positive than those > or = 1.0 cm (p = 0.037). No significant differences in surgical discipline, hospital site, number of slides, or cross-sections/cm artery were found between the positive and negative biopsies. CONCLUSION: Biopsy specimens reported positive for GCA tended to be longer than those reported as negative. A "threshold" size of 1.0 cm is associated with increased diagnostic yield. Lack of standardization of biopsy harvesting and processing techniques may contribute to variable sensitivity of TAB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".